Abstract
In this paper, a maintenance prediction model is developed for school building assets using a large data set provided by the Queensland Department of Education and Training (DET). DET data on the asset condition, historical maintenance expenditure, and asset characteristics, was analyzed to evaluate which characteristics affect the maintenance needs of the school assets. The condition of the assets was quantified using data on the estimated maintenance backlog. Using statistical methods, models for key building element groups were constructed and the statistical significance of each factor was evaluated. It was found that the school region, the gross floor area, and the maintenance expenditure significantly affected the degradation of key building element groups.
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Notes
- 1.
We actually use the moving average of the past 4 years of maintenance expenditures due to the infrequent inspection intervals and the challenges with aligning the precise dates of maintenance with the inspection times.
- 2.
One could also use the number of students to normalize the costs. However, motivated by our later statistical analysis, we only show GFA.
References
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Acknowledgments
This project was funded by the Queensland Department of Education and Training (DET). The authors wish to thank Ariane Panochini, Greg Duck, Malvin White, and Nadeia Romanowski for the in-depth discussions and insight which greatly aided the research presented in this paper.
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Wang, R., Cholette, M.E., Ma, L. (2018). Predicting Maintenance Requirements for School Assets in Queensland. In: Zuo, M., Ma, L., Mathew, J., Huang, HZ. (eds) Engineering Asset Management 2016. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-62274-3_25
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DOI: https://doi.org/10.1007/978-3-319-62274-3_25
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